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2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Harini, V.K. Bhavadharini, R.M. Upreti, Charvi Alex, Libin Schoo of Computer Science and Engineering Vellore Institute of Technology Chennai India Schoo of Computer Science and Engineering Vellore Institute of technology India

ISBN: (纸本)9798350374711

As per a 2023 publication, addressing mental health problems in India holds immense significance due to the scale of human value impact involved, particularly given the country's population size. It is crucial to recognize that mental health issues affect a significant portion of the population and can lead to severe consequences if left unaddressed. With this paper, we aim to contribute to the advancement of mental healthcare provision by developing a task-oriented, closed-domain chatbot. This chatbot will serve as a valuable tool for individuals seeking support and information regarding mental health issues. The paper compares existing models which use various embeddings and transformers and focuses on the development of a task-oriented, closed-domain chatbot using a retrieval-based approach with text data. The maximum accuracy of 91.48% was achieved in the LSTM model. © 2024 IEEE.

关键词： bag-of-words chatbot closed domain mental health RoBERTa task oriented model word2vec

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Cross Domain Transfer Network for State of Charge Estimation of Li-On Batteries 20

Cross Domain Transfer Network for State of Charge Estimation...

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20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Chongming, Yang Schoo of Computer Science and Technology University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Deep learning methods have garnered considerable inter-est in State of Charge (SOC) estimation due to their capabilities to capture complex temporal patterns. However, the success of these methods is based on the labelled target data and the pre-sumption that both the training and testing data originate from identical distributions. In the real word, the operating conditions of batteries are diverse, which makes existing methods in-valid. To address this challenge, a cross domain transfer network (CDTN) with multiscale distribution adaptation (MDA) is proposed to reduce domain distances and align high-dimensional features. In this method, a feature extractor incor-porating convolutional neural network (CNN) and bidirectional long-short term memory (Bi-LSTM) is utilized. In the transfer learning stage, adversarial adaptation and MDA are combined to minimize the distribution discrepancy. Through extensive experiments, the results show the adapted CDTN achieves the best performance under practical discharge applications, which can prevent the batteries from over-discharging and prolong the lifespan effectively. © 2023 IEEE.

关键词： Long short-term memory

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Mental Health Chatbot

Mental Health Chatbot

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Students Conference on Engineering and Systems (SCES)

作者： Harini V K Bhavadharini R M Charvi Upreti Libin Alex Schoo of Computer Science and Engineering Vellore Institute of Technology Chennai India Schoo of Computer Science and Engineering Vellore Institute of technology City Country

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

关键词： Training Accuracy Mental health Medical services Chatbots Transformers Data models

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Cross Domain Transfer Network For State Of Charge Estimation Of Li-On Batteries

Cross Domain Transfer Network For State Of Charge Estimation...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Yang Chongming Schoo of Computer Science and Technology University of Electronic Science and Technology of China Chengdu

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 4

Customer Segment Classification Prediction in the Australian...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luo, Yiming Zhang, Rui Wang, Fei Wei, Tao Business Schoo The University of Sydney Sydney Australia Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China School of Chemical and Environmental Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment. © 2023 ACM.

关键词： Customer satisfaction

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Spatiotemporal Feature Extraction and Fusion for Longitudinal Alzheimer’s Disease Diagnosis 17th

Spatiotemporal Feature Extraction and Fusion for Longitudina...

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17th International Conference on Brain Informatics, BI 2024

作者： Guan, Zhenghua Yang, Peng Lei, Haijun Yang, Bao Song, Xuegang Dong, Lei Yan, Xueqin Wei, Cuimei Liang, Chunhua Xiao, Xiaohua Wang, Tianfu Lei, Baiying School of Biomedical Engineering Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Shenzhen University Medical schoo1 Shenzhen518060 China Affiliated Hospital of Shenzhen University Health Science Center Shenzhen University Shenzhen Second People’s Hospital Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789819632961

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. The brain structure of AD patients progressively atrophies over time. Longitudinal data can capture the changes in imaging over time, providing important information for early detection and diagnosis of the disease, which is not achievable with images from a single time point. We propose a novel longitudinal AD diagnosis model that leverages deformation field data obtained by registering images from adjacent time points. Each pixel in the deformation field represents the displacement vector of the corresponding pixel between two scans, reflecting information about brain tissue atrophy. By effectively extracting both temporal and spatial features from the longitudinal data, our model enhances early AD diagnosis. The input data for the model consists of processed sMRI images and deformation fields calculated between each pair of time points in the longitudinal sMRI data. The model is composed of three modules: Convolutional Long Short-Term Memory (CLSTM), Gate Fusion Long Short-Term Memory (GFLSTM), and an attention module. CLSTM extracts temporal features while preserving spatial features by maintaining the 3D shape of the features, GFLSTM achieves spatiotemporal feature fusion within a cascaded network, and the attention module further enhances feature representation. Our proposed method was evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and experiments demonstrate that it outperforms several state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Don't worry about mistakes! Glass Segmentation Network via Mistake Correction

arXiv

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arXiv 2023年

作者： Zheng, Chengyu Li, Peng Zhang, Xiao-Ping Lu, Xuequan Wei, Mingqiang The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada The Schoo of Information Technology Deakin University VIC Australia

Recall one time when we were in an unfamiliar mall. We might mistakenly think that there exists or does not exist a piece of glass in front of us. Such mistakes will remind us to walk more safely and freely at the same or a similar place next time. To absorb the human mistake correction wisdom, we propose a novel glass segmentation network to detect transparent glass, dubbed GlassSegNet. Motivated by this human behavior, GlassSegNet utilizes two key stages: the identification stage (IS) and the correction stage (CS). The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information. The CS then progressively refines the coarse prediction by correcting mistake regions based on gained experience. Extensive experiments show clear improvements of our GlassSegNet over thirty-four state-of-the-art methods on three benchmark datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Glass

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 23

Customer Segment Classification Prediction in the Australian...

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Proceedings of the 2023 4th International Conference on Machine Learning and computer Application

作者： Yiming Luo Rui Zhang Fei Wang Tao Wei Business Schoo The University of Sydney Austialia Computer and information sciences program Harrisburg University of Science and Technology United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College China School of Chemical and Environmental Engineering Sichuan University of Science & Engineering China

ISBN: (纸本)9798400709449

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Conflict Analysis and Detection Based on Model Checking for Spatial Access Control Policy

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Tsinghua science and technology 2017年第5期22卷 478-488页

作者： Aijuan Zhang Cheng Ji Yu Bao Xin Li the School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China the Schoo1 of Environment Science and Spatial Inform atics China University of M ining and TechnologyXuzhou 221116China

In this paper, we propose a Multi-granularity Spatial Access Control （MSAC） model, in which multi- granularity spatial objects introduce more types of policy rule conflicts than single-granularity objects do. To analyze and detect these conflicts, we first analyze the conflict types with respect to the relationship among the policy rules, and then formalize the conflicts by template matrices. We designed a model-checking algorithm to detect potential conflicts by establishing formalized matrices of the policy set. Lastly, we conducted experiments to verify the performance of the algorithm using various spatial data sets and rule sets. The results show that the algorithm can detect all the formalized conflicts. Moreover, the algorithm＇s efficiency is more influenced by the spatial object granularity than the size of the rule set.

关键词： spatial object multi-granularity conflict detection model-checking

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A Simple Two-Level Bayesian Network and its Application in the HPLC-MS Data

A Simple Two-Level Bayesian Network and its Application in t...

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第2届大连国际色谱学术报告会、第37届国际高效液相色谱及相关技术会议、第18届全国色谱学术报告会

作者： Ping Ma Xiaohui Lin Peivuan Yin Guowang Xu Schoo1 of Computer Science and Technology Dalian University of Technology Dalian 116024 China CAS Key Laboratory of Separation Science for Analytical Chemistry Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 China

Bayesian network (BN) [1] is a very simple and efficient classifier, and has a wide application in many *** its structure learning is quite difficult, especially for high dimensional data, such as liquid chromatography-mass spectrometry (LC-MS) *** the number of the possible structures grows exponentially with the number of *** process LC-MS data effectively, we applied an efficient BN structure learning method which constructs a two-level BN (BN-TwoL).BN-TwoL sets the class label C as the root of the *** variables which are conditional independent of each other lie in different sub-trees of the root.

关键词： Classification Bayesian Networks Liquid Chromatography

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